Impact of heterologous expression of Cannabis sativa tetraketide synthase on Phaeodactylum tricornutum metabolic profile
Bibliographic record
Abstract
BACKGROUND: Pharmaceutical safety is an increasing global priority, particularly as the demand for therapeutic compounds rises alongside population growth. Phytocannabinoids, a class of bioactive polyketide molecules derived from plants, have garnered significant attention due to their interaction with the human endocannabinoid system, offering potential benefits for managing a range of symptoms and conditions. Traditional extraction from cannabis plants poses regulatory, environmental, and yield-related challenges. Consequently, microbial biosynthesis has emerged as a promising biotechnological alternative to produce cannabinoids in a controlled, scalable, and sustainable manner. Developing diatom-based biofactories represent a crucial step in advancing this biotechnology, enabling the efficient production of high-valued compounds such as cannabinoids. RESULTS: We engineered the diatom Phaeodactylum tricornutum, a unicellular photosynthetic model organism prized for its naturally high lipid content, to produce olivetolic acid (OA), a key metabolic precursor to most cannabinoids. The genes encoding tetraketide synthase and olivetolic acid cyclase from cannabis were cloned onto episomal vectors and introduced using bacterial conjugation in two separate P. tricornutum transconjugant lines to evaluate enzyme activity and OA production in vivo. Both genes were successfully expressed, and the corresponding enzymes accumulated within the transconjugant lines. However, despite testing the cell extracts individually and in combination, OA accumulation was not detected suggesting potential conversion or utilization of OA by endogenous metabolic pathways within the diatoms. To investigate this further, we analyzed the impact of CsTKS expression on the diatom's metabolome, revealing significant alterations that may indicate metabolic flux redirection or novel pathway interactions. CONCLUSIONS: Our study demonstrates the successful expression of cannabinoid biosynthetic genes in P. tricornutum but highlights challenges in OA accumulation, likely due to endogenous metabolic interactions. These findings underscore the complexity of metabolic engineering in diatoms and suggest the need for further pathway optimization and metabolic flux analysis to achieve efficient cannabinoid biosynthesis. This research contributes to advancing sustainable biotechnological approaches for cannabinoid production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".